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Machine Learning for Emotion Analysis in Python by Allan Ramsay PDF



Kickstart your emotion analysis journey with this step-by-step guide to data science success



Key Features

Discover the inner workings of the end-to-end emotional analysis workflow

Explore the use of various ML models to derive meaningful insights from data

Hone your craft by building and tweaking complex emotion analysis models with practical projects

Purchase of the print or Kindle book includes a free PDF eBook

Book Description

Artificial intelligence and machine learning are the technologies of the future, and this is the perfect time to tap into their potential and add value to your business. Machine Learning for Emotion Analysis in Python helps you employ these cutting-edge technologies in your customer feedback system and in turn grow your business exponentially.



With this book, you’ll take your foundational data science skills and grow them in the exciting realm of emotion analysis. By following a practical approach, you’ll turn customer feedback into meaningful insights assisting you in making smart and data-driven business decisions.



The book will help you understand how to preprocess data, build a serviceable dataset, and ensure top-notch data quality. Once you’re set up for success, you’ll explore complex ML techniques, uncovering the concepts of deep neural networks, support vector machines, conditional probabilities, and more. Finally, you’ll acquire practical knowledge using in-depth use cases showing how the experimental results can be transformed into real-life examples and how emotion mining can help track short- and long-term changes in public opinion.



By the end of this book, you’ll be well-equipped to use emotion mining and analysis to drive business decisions.



What you will learn

Distinguish between sentiment analysis and emotion analysis

Master data preprocessing and ensure high-quality input

Expand the use of data sources through data transformation

Design models that employ cutting-edge deep learning techniques

Discover how to tune your models’ hyperparameters

Explore the use of naive Bayes, SVMs, DNNs, and transformers for advanced use cases

Practice your newly acquired skills by working on real-world scenarios

Who this book is for

This book is for data scientists and Python developers looking to gain insights into the customer feedback for their product, company, brand, governorship, and more. Basic knowledge of machine learning and Python programming is a must.



Table of Contents

Foundations

Building and Using a Dataset

Labelling Data

Preprocessing - Stemming, Tagging, and Parsing

Sentiment Lexicons and Vector-Space Models

Naïve Bayes

Support Vector Machines

Neural Networks and Deep Neural Networks

Exploring Transformers

Multiclassifiers

Case Study - The Qatar Blockade



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